Zlabinger Markus, Andersson Linda, Brassey Jon, Hanbury Allan
Institute of Software Technology and Interactive Systems, TU Wien, Vienna.
Trip Database Ltd., UK.
Stud Health Technol Inform. 2018;247:146-150.
In this paper, an identification approach for the Population (e.g. patients with headache), the Intervention (e.g. aspirin) and the Comparison (e.g. vitamin C) in Randomized Controlled Trials (RCTs) is proposed. Contrary to previous approaches, the identification is done on a word level, rather than on a sentence level. Additionally, we classify the sentiment of RCTs to determine whether an Intervention is more effective than its Comparison. Two new corpora were created to evaluate both approaches. In the experiments, an average F1 score of 0.85 for the PIC identification and 0.72 for the sentiment classification was achieved.
本文提出了一种在随机对照试验(RCT)中识别总体(如头痛患者)、干预措施(如阿司匹林)和对照(如维生素C)的方法。与以往的方法不同,该识别是在单词层面上进行的,而不是在句子层面上。此外,我们对随机对照试验的情感进行分类,以确定一种干预措施是否比其对照更有效。创建了两个新的语料库来评估这两种方法。在实验中,PIC识别的平均F1分数为0.85,情感分类的平均F1分数为0.72。